Handling Big Data Scalability in Biological Domain Using Parallel and Distributed Processing: A Case of Three Biological Semantic Similarity Measures

Semantic similarity is an essential concept that widen across various fields such as artificial intelligence, natural language processing, information retrieval, relation extraction, document clustering and automatic data extraction. The study proposed in this paper focus on semantic similarity and counter measure on question answering task. Despite the usefulness of semantic similarity in these applications, finding the exact meaning between the words from question and answer pair has become a major challenge in question answering system. To recognize various relationships those exist between question and answer pair, measuring semantically is essential. We present study using a match maker algorithm for similarity measurement between question-answer pair. The study compares similarity measurement using match maker algorithm as against semantic similarity measure on Miller-Charles bench marked data set. This paper present study about different types of algorithm used to measure the semantic similarity between the words and those result compare with match maker algorithm. The experiments on real datasets shows matchmaker algorithm works better than web-based semantic similarity measure.

Several approaches for computing semantic similarity and relatedness measures between terms have been developed. This paper proposes a new semantic similarity measure between two nodes concentrating on nouns as well as their hypernym/hyponym relationships based on the structure of Wordnet. In particular, the similarity of two given nouns depends not only on their positions but also on their relevancy connections in the hierarchy. We evaluate our measure and the other ones on dataset of Miller-Charles and then compute the correlation coefficients to the human judgments. The experimental results show that our method outperforms edge-counting methods.

Several measures have been recently proposed for quantifying the functional similarity between gene products according to well-structured controlled vocabularies where biological terms are organized in a tree or in a directed acyclic graph (DAG) structure. However, existing semantic similarity measures ignore two important facts. First, when calculating the similarity between two terms, they disregard the descendants of these terms. While this makes no difference when the ontology is a tree, we shall show that it has important consequences when the ontology is a DAG-this is the case, for example, with the Gene Ontology (GO). Second, existing similarity measures do not model the inherent uncertainty which comes from the fact that our current knowledge of the gene annotation and of the ontology structure is incomplete. Here, we propose a novel approach based on downward random walks that can be used to improve any of the existing similarity measures to exhibit these two properties. The approach is computationally efficient-random walks do not need to be simulated as we provide formulas to calculate their stationary distributions. To show that our approach can potentially improve any semantic similarity measure, we test it on six different semantic similarity measures: three commonly used measures by Resnik (1999), Lin (1998), and Jiang and Conrath (1997); and three recently proposed measures: simUI, simGIC by Pesquita et al. (2008); GraSM by Couto et al. (2007); and Couto and Silva (2011). We applied these improved measures to the GO annotations of the yeast Saccharomyces cerevisiae, and tested how they correlate with sequence similarity, mRNA co-expression and protein-protein interaction data. Our results consistently show that the use of downward random walks leads to more reliable similarity measures.

WITHDRAWN: Psycholinguistic evaluation of ASC based semantic similarity measures

Clustering of genes on the basis of expression profiles is generally the first step in understanding how a class of genes behaves in a biological process. A number of supervised and unsupervised algorithms are available in statistics and machine learning literature for clustering microarray data, but the algorithms are restricted in their ability to evaluate the results of a clustering algorithm in the light of biologically meaningful clusters. If two gene sequences are similar, then we would expect that their genetic expressions are similar and that they are similarly annotated in the Gene Ontology (GO) databases. Hence a comparison of the expression level similarity of two gene sequences against their corresponding similarity of annotation in the GO can establish this fact. Semantic similarity has now become a valuable tool for validating the results drawn from biomedical studies such as gene clustering and gene expression data analysis. This paper borrows from our previous work on meta-ensembles using cancer datasets where the output of several clustering algorithms are subsequently fed to a consensus building process to generate a stable set of cluster results. Next, these cluster results are further refined through a sequence of biological validation process for each gene pair of a given cluster using semantic similarity and sequence similarity. We have tested our approach on several benchmark cancer datasets in an attempt to provide a more accurate biological analysis of the clusters and the results have been found to be satisfactory.

It is necessary to find a computational method for prediction of protein subcellular location (SCL). Many researches have focused on the topic. Among them, methods incorporated Gene Ontology (GO) achieved higher prediction accuracy. However the former method of extracting features from GO have some disadvantages. In this paper, to increase the accuracy of the prediction, we present a novel method to extract features from GO by semantic similarity measurement, which is hopeful to overcome the disadvantages of former method. Testing on a public available dataset shows satisfied results. And this method can also be used in similar scenarios in other bioinformatics researches or data mining process.

Background Although biomedical ontologies have standardized the representation of gene products across species and databases, a method for determining the functional similarities of gene products has not yet been developed. Methods We proposed a new semantic similarity measure based on Gene Ontology that considers the semantic influences from all of the ancestor terms in a graph. Our measure was compared with Resnik's measure in two applications, which were based on the association of the measure used with the gene co-expression and the protein-protein interactions. Results The results showed a considerable association between the semantic similarity and the expression correlation and between the semantic similarity and the protein-protein interactions, and our measure performed the best overall. Conclusion These results revealed the potential value of our newly proposed semantic similarity measure in studying the functional relevance of gene products.

Fuzzy control GA with a novel hybrid semantic similarity strategy for text clustering

A key challenge for document clustering consists in finding a proper similarity measure for text documents that enables the generation of cohesive groups. Measures based on the classic bag-of-words model take into account solely the presence (and frequency) of words in documents. In doing so, semantically similar documents which use different vocabularies may end up in different clusters. For this reason, semantic similarity measures that use external knowledge, such as word n-gram corpora or thesauri, have been proposed in the literature. In this paper, the Frequency Google Tri-gram Measure is proposed to assess similarity between documents based on the frequencies of terms in the compared documents as well as the Google n-gram corpus as an additional semantic similarity source. Clustering algorithms are applied to several real datasets in order to experimentally evaluate the quality of the clusters obtained with the proposed measure and compare it with a number of state-of-the-art measures from the literature. The experimental results demonstrate that the proposed measure improves significantly the quality of document clustering, based on statistical tests. We further demonstrate that clustering results combining bag-of-words and semantic similarity are superior to those obtained with either approach independently.

BackgroundOntologies are widely used as metadata in biological and biomedical datasets. Measures of semantic similarity utilize ontologies to determine how similar two entities annotated with classes from ontologies are, and semantic similarity is increasingly applied in applications ranging from diagnosis of disease to investigation in gene networks and functions of gene products.ResultsHere, we analyze a large number of semantic similarity measures and the sensitivity of similarity values to the number of annotations of entities, difference in annotation size and to the depth or specificity of annotation classes. We find that most similarity measures are sensitive to the number of annotations of entities, difference in annotation size as well as to the depth of annotation classes; well-studied and richly annotated entities will usually show higher similarity than entities with only few annotations even in the absence of any biological relation.ConclusionsOur findings may have significant impact on the interpretation of results that rely on measures of semantic similarity, and we demonstrate how the sensitivity to annotation size can lead to a bias when using semantic similarity to predict protein-protein interactions.

Gene Ontology (GO) annotations encode scientific knowledge that states the properties of GO-based annotated entities, e.g., proteins, enzymes, or genes. On one hand, taxonomic knowledge, which is encoded in class hierarchies, states the abstract types of the annotations. On other hand, semantics of the object properties expressed as logic axioms, determine the facts, inferred or stated, where the annotations participate in the ontology, i.e., the annotation neighborhood. Further, informativeness of a term in a corpus allows for the characterization of the specificity of the GO terms used in a dataset of GO-based annotated entities. We hypothesize that the combination of all these characteristics provides the basis for an accurate estimation of the similarity between GO-based annotated entities; we devise thus a novel semantic similarity measure named IC-OnSim. IC-OnSim considers all informativeness, the semantics encoded in the ontology, and the taxonomic information as first-class citizens, and differentiates annotations that are taxonomically similar, but that either are not equally informative or have different neighborhoods in GO. IC-OnSim determines semantics based on the neighborhood facts of the corresponding GO terms, i.e., the object properties where these GO facts participate and the justifications that support the entailment of these facts; further, informativeness of a term is measured as Information Content (IC). We empirically study the performance of IC-OnSim on benchmarks of GO-based annotated proteins published by the Collaborative Evaluation of Semantic Similarity Measures (CESSM) tool. During the evaluation, IC-OnSim exhibits the highest values of the Pearson's correlation coefficients with respect to the gold standard similarity measures Sequence Similarity and Pfam. These results support our hypothesis that the combination of the properties of the GO terms allows for the precise computation of the relatedness of GO-based annotated entities.

Semantic similarity is a metric used to know the similarity degree of two concepts in an ontology or a taxonomy. Semantic similarity has a wide variety applications on artificial intelligence, natural language processing, biomedical informatics, geoinformatics and semantic web and is usually applied on machine translation and word-sense disambiguation. In this research, semantic similarity measures are used to evaluate taxonomic relationships in a domain ontology. This evaluation was carried out by using a proposed algorithm and through the accuracy measure. The semantic similarity measures implemented are based on information content and were proposed by the following authors: Resnik, Lin, Jiang & Conrath and Mazandu & Mulder. Mainly, this research contributes to the automatic evaluation of ontologies in the task of evaluating the ontology taxonomy. The experimental results show that the measures have at least 88% accuracy. In addition, the system has an accuracy of 94% compared to validation responses from an expert.

Finding most informative common ancestor in cross-ontological semantic similarity assessment: An intrinsic information content-based approach

One of the most significant difficulties in information retrieval is measuring semantic similarity. Semantically similar words, phrases, and concepts are measured in this field of study. It is the degree to which two concepts resemble one another depending on their meaning. Various measures of semantic similarity have been suggested several times throughout the years. This paper divides these methodologies into two groups: knowledge-based and corpus-based, to demonstrate how they evolved. On specified parameters, a study of similarity approaches is performed. This assessment, which analyzes each method for semantic similarity, provides an analysis, review of existing methods on semantic similarity to explore, and creates new ideas for researchers.KeywordsSemantic similaritySemantic relatednessKnowledge-based methodsCorpus-based methodsInformation retrieval

Determining the extent to which two text snippets are semantically equivalent is a well-researched topic in the areas of natural language processing, information retrieval and text summarization. The sentence-to-sentence similarity scoring is extensively used in both generic and query-based summarization of documents as a significance or a similarity indicator. Nevertheless, most of these applications utilize the concept of semantic similarity measure only as a tool, without paying importance to the inherent properties of such tools that ultimately restrict the scope and technical soundness of the underlined applications. This paper aims to contribute to fill in this gap. It investigates three popular WordNet hierarchical semantic similarity measures, namely path-length, Wu and Palmer and Leacock and Chodorow, from both algebraical and intuitive properties, highlighting their inherent limitations and theoretical constraints. We have especially examined properties related to range and scope of the semantic similarity score, incremental monotonicity evolution, monotonicity with respect to hyponymy/hypernymy relationship as well as a set of interactive properties. Extension from word semantic similarity to sentence similarity has also been investigated using a pairwise canonical extension. Properties of the underlined sentence-to-sentence similarity are examined and scrutinized. Next, to overcome inherent limitations of WordNet semantic similarity in terms of accounting for various Part-of-Speech word categories, a WordNet “All word-To-Noun conversion” that makes use of Categorial Variation Database (CatVar) is put forward and evaluated using a publicly available dataset with a comparison with some state-of-the-art methods. The finding demonstrates the feasibility of the proposal and opens up new opportunities in information retrieval and natural language processing tasks.